We compare these results with KNN (K-nearest neighbor) classifier with \(k=5\)  estimators from \(\mathop {\mathtt {sklearn}}\)  library, using 12 different distances: Continuous Fréchet Distance [1]}, Discrete Fréchet Distance [2]}, Hausdorff Distance [3]}, DTW (Dynamic Time Warping) [4]}, fastdtw (an approximation for Dynamic Time Warping) [5]}, soft-dtw (Soft Dynamic Time Warping) [6]}, SSPD (Symmetric Segment-Path Distance) [7]}, LCSS (Longest Common Subsequence) [8]}, EDR (Edit Distance on Real sequence) [9]}, ERP (Edit distance with Real Penalty) [10]}, \(\mathtt {d}_{Q}^{\pi }\)  [11]} and LSH (computed by binary sketches for Hausdorff distance) [12]}. The soft-dtw distance is implemented from GitHub page https://github.com/mblondel/soft-dtw, fastdtw from PyPI, DTW from \(\mathop {\mathtt {tslearn}}\) , \(\mathtt {d}_{Q}^{\pi }\)  from \(\mathtt {trjtrypy}\)  package by the authors in PyPI, LSH is implemented by the authors, and the rest of the distances use efficient code available from the GitHub page https://github.com/bguillouet/traj-dist. In all experiments with KNN using LSH, we have applied 20 random circles to get binary sketches utilized in LSH distance. To choose the circles' radius \(r\) , like computing \(\eta \)  in the mistake-driven algorithm, we set \(r = \Vert r_1 - r_2\Vert \) , where \(r_1\)  and \(r_2\)  are the mean of \((x,y)\) -coordinates of trajectories in the \(70\%\)  of the first and second datasets used as training data. Finally, in all landmark-based experiments in this paper we use 20 landmarks. Indeed, in random \(v_Q\) , \(v_Q^{\exp }\)  and \(v_Q^{\varsigma }\)  we randomly generate 20 landmarks around the curves. For mistake-driven approaches, Algorithm REF  chooses 20 landmarks accordingly.
To make it easy to reproduce or compare against our results, we link to all data sets, and host cleaning and testing methods on a public github page https://github.com/aghababa/Classifying-Spatial-Trajectories.
